metasurvey

metasurvey provides tools to streamline the analysis of complex survey data using metaprogramming and reproducible pipelines.
It integrates tightly with the survey package and is especially helpful when working with complex survey designs and recurrent estimations over time (e.g., rotating panels or repeated cross-sections).

Key features:

• Tidy-style API for survey processing and estimation.
• Reproducible survey pipelines with steps and workflows.
• Support for advanced variance estimation methods (e.g., bootstrap replicates).
• Recipes and reusable metadata to manage harmonized survey definitions over time.

---

Installation

Install the stable version from CRAN:

install.packages("metasurvey")

Or the development version from GitHub:

# install.packages("devtools")
devtools::install_github("metasurveyR/metasurvey")

---

Quick example

library(metasurvey)
library(magrittr)

ech_2022 <- load_survey(
    metasurvey::load_survey_example(
        "ech",
        "ech_2022"
    ),
    svy_edition = "2022",
    svy_type = "ech",
    svy_weight = add_weight(annual = "w_ano"),
    recipes = get_recipe(
        "ech",
        "2022"
    )
)

---

Full panel example (with bootstrap replicates)

This example uses the Uruguayan ECH panel with bootstrap replicate weights.
First, download and unzip the example data:

curl -o example-data.zip "https://informe-tfg.s3.us-east-2.amazonaws.com/example-data.zip"
mkdir example-data
mkdir temp && unzip example-data.zip -d temp && mv temp/example-data/* example-data/ && rm -rf temp
rm example-data.zip

Or run the following R script to download and unzip the data:

# download_example_data.R

# This script downloads and unzips the example-data.zip from S3

download_example_ech <- function() {
  zip_url <- "https://informe-tfg.s3.us-east-2.amazonaws.com/example-data.zip"
  dest_zip <- "example-data.zip"
  temp_dir <- tempfile("example-data")

  # Download the zip file
  download.file(zip_url, destfile = dest_zip, mode = "wb")
  message("Downloaded example-data.zip")

  # Unzip to temporary directory
  dir.create(temp_dir)
  unzip(dest_zip, exdir = temp_dir)
  message("Unzipped example-data.zip")

  target_dir <- file.path("example-data")
  dir.create(target_dir, recursive = TRUE, showWarnings = FALSE)
  file.rename(list.files(file.path(temp_dir, "example-data"), full.names = TRUE), 
              file.path(target_dir, basename(list.files(file.path(temp_dir, "example-data")))))
  message("Moved unzipped data to chapters/example-data")

  # Cleanup
  unlink(dest_zip)
  unlink(temp_dir, recursive = TRUE)
  message("Cleanup done")
}

# Run the function
download_example_ech()

Then, you can run the following analysis:

library(metasurvey)
library(magrittr)

path_dir <- file.path("example-data", "ech", "ech_2023")

ech_2023 <- load_panel_survey(
  path_implantation = file.path(path_dir, "ECH_implantacion_2023.csv"),
  path_follow_up = file.path(path_dir, "seguimiento"),
  svy_type = "ECH_2023",
  svy_weight_implantation = add_weight(annual = "W_ANO"),
  svy_weight_follow_up = add_weight(
    monthly = add_replicate(
      "W",
      replicate_path = file.path(
        path_dir,
        c(
          "Pesos replicados Bootstrap mensuales enero_junio 2023",
          "Pesos replicados Bootstrap mensuales julio_diciembre 2023"
        ),
        c(
          "Pesos replicados mensuales enero_junio 2023",
          "Pesos replicados mensuales Julio_diciembre 2023"
        )
      ),
      replicate_id = c("ID" = "ID"),
      replicate_pattern = "wr[0-9]+",
      replicate_type = "bootstrap"
    )
  )
)

ech_2023 <- ech_2023 %>%
  step_recode("pea", POBPCOAC %in% 2:5 ~ 1, .default = 0, comment = "PEA", .level = "follow_up") %>%
  step_recode("pet", e27 >= 14 ~ 1, .default = 0, comment = "PET", .level = "follow_up") %>%
  step_recode("po", POBPCOAC == 2 ~ 1, .default = 0, comment = "PO", .level = "follow_up") %>%
  step_recode("pd", POBPCOAC %in% 3:5 ~ 1, .default = 0, comment = "PD", .level = "follow_up")

ech_2023_bake <- bake_steps(ech_2023)

workflow_result <- workflow(
  survey = extract_surveys(ech_2023_bake, quarterly = 1:4),
  survey::svyratio(~pea, denominator = ~pet),
  survey::svyratio(~po, denominator = ~pet),
  survey::svyratio(~pd, denominator = ~pea),
  estimation_type = "quarterly:monthly",
  rho = 0.5,
  R = 5 / 6
)

workflow_result